AUCell KPMP Stroma
Jonathan Nelson
15 June, 2025
1 Load Packages
if (!require("knitr")) {install.packages("knitr"); require("knitr")}
if (!requireNamespace('BiocManager', quietly = TRUE)) {install.packages('BiocManager'); require("BiocManager")}
if (!require("dplyr")) {install.packages("dplyr"); require("dplyr")}
if (!require("stringr")) {install.packages("stringr"); require("stringr")}
if (!require("Seurat")) {install.packages("Seurat"); require("Seurat")}
if (!require("sctransform")) {install.packages("sctransform"); require("sctransform")}
if (!require("glmGamPoi")) {BiocManager::install('glmGamPoi'); require("glmGamPoi")}
if (!require("patchwork")) {install.packages("patchwork"); require("patchwork")}
if (!require("ggplot2")) {install.packages("ggplot2"); require("ggplot2")}
if (!require("EnhancedVolcano")) {BiocManager::install('EnhancedVolcano'); require("EnhancedVolcano")}
if (!require("DESeq2")) {BiocManager::install('DESeq2'); require("DESeq2")}
if (!require("tidyverse")) {install.packages("tidyverse"); require("tidyverse")}
if (!require("RColorBrewer")) {install.packages("RColorBrewer"); require("RColorBrewer")}
if (!require("car")) {install.packages("car"); require("car")}
if (!require("openxlsx")) {install.packages("openxlsx"); require("openxlsx")}
if (!require("readxl")) {install.packages("readxl"); require("readxl")}
if (!require("ggrepel")) {install.packages("ggrepel"); require("ggrepel")}
if (!require("gghighlight")) {install.packages("gghighlight"); require("gghighlight")}
if (!require("ggpmisc")) {install.packages("ggpmisc"); require("ggpmisc")}
if (!require("data.table")) {install.packages("data.table"); require("data.table")}
if (!require("here")) {install.packages("here"); require("here")}
options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 79456894976#if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BiocManager")
# To support paralell execution:
#BiocManager::install(c("doMC", "doRNG","doSNOW"))
# For the main example:
#BiocManager::install(c("mixtools", "SummarizedExperiment"))
# For the examples in the follow-up section of the tutorial:
#BiocManager::install(c("DT", "plotly", "NMF", "d3heatmap",
# "dynamicTreeCut", "R2HTML", "Rtsne", "zoo"))
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("AUCell")
library(AUCell)
library(GSEABase)
here()## [1] "C:/Users/jonat/OneDrive - University of Southern California/Github/jga"2 Loading Filtered Seurat Object
These files are post-DoubletFinder.
## 5 x 5 sparse Matrix of class "dgCMatrix"
## KB1_AAACCCAAGGTTATAG KB1_AAACCCATCAGGTGTT KB1_AAACCCATCTTAAGGC
## A1BG . . .
## A1BG-AS1 . 1.292535 0.9851203
## A1CF . . .
## A2M 1.684869 1.292535 1.7974767
## A2M-AS1 . . .
## KB1_AAACGCTGTGTCCACG KB1_AAATGGAGTCATTGCA
## A1BG . .
## A1BG-AS1 . .
## A1CF . .
## A2M . .
## A2M-AS1 . .cells_AUC <- AUCell_run(exprMatrix, geneSets)
cells_rankings <- AUCell_buildRankings(exprMatrix, plotStats=FALSE)
cells_AUC <- AUCell_calcAUC(geneSets, cells_rankings)
cells_rankings <- AUCell_buildRankings(exprMatrix, plotStats=TRUE)
## min 1% 5% 10% 50% 100%
## 401.00 426.00 493.95 573.00 1397.00 7368.00## Ranking for 29732 genes (rows) and 18740 cells (columns).
##
## Top-left corner of the ranking:
## cells
## genes KB1_AAACCCAAGGTTATAG KB1_AAACCCATCAGGTGTT KB1_AAACCCATCTTAAGGC
## A1BG 26654 14059 22592
## A1BG-AS1 5952 1334 2282
## A1CF 25856 16477 17713
## A2M 1255 2114 474
## A2M-AS1 17460 23082 5417
## A2ML1 19914 25598 22810
## cells
## genes KB1_AAACGCTGTGTCCACG KB1_AAATGGAGTCATTGCA
## A1BG 26186 9748
## A1BG-AS1 13354 7487
## A1CF 23758 22995
## A2M 16507 13809
## A2M-AS1 12037 9582
## A2ML1 16993 13977
##
## Quantiles for the number of genes detected by cell:
## min 1% 5% 10% 50% 100%
## 401.00 426.00 493.95 573.00 1397.00 7368.00cells_AUC <- AUCell_calcAUC(geneSets, cells_rankings)
set.seed(333)
par(mfrow=c(3,3))
cells_assignment <- AUCell_exploreThresholds(cells_AUC, plotHist=TRUE, assign=TRUE)
## Create Common Metadata Columns
cellsAssigned <- lapply(cells_assignment, function(x) x$assignment)
assignmentTable <- reshape2::melt(cellsAssigned, value.name="cell")
colnames(assignmentTable)[2] <- "geneSet"
head(assignmentTable)## [1] TRUE# assignmentTable$cell[duplicated(assignmentTable$cell)]
assignmentTable <- assignmentTable[!duplicated(assignmentTable$cell), ]
# Remove existing row names
rownames(assignmentTable) <- NULL
# Now convert "cell" column to row names
assignmentTable <- assignmentTable %>% column_to_rownames(var = "cell")
SO <- AddMetaData(SO, assignmentTable)
SO@meta.data
##
## h_afferent h_efferent h_fiba h_fibb h_fibc h_fibd h_pericyte h_renin
## aFIB 0 1 1 6 128 0 0 0
## cycMYOF 0 0 0 1 0 0 0 0
## dFIB 0 0 0 0 0 0 0 0
## dM-FIB 0 0 0 0 0 0 0 0
## dVSMC 1 0 0 0 0 0 0 0
## FIB 0 0 148 47 58 0 0 0
## M-FIB 1 0 112 539 0 389 0 0
## MC 0 2 0 0 0 0 0 5
## MYOF 122 43 8 29 38 0 1 1
## REN 2 6 0 0 0 0 1 5
## VSMC 110 366 0 0 0 0 74 5
## VSMC/P 8 90 2 2 0 4 103 43 Session Info
## R version 4.4.3 (2025-02-28 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GSEABase_1.68.0 graph_1.84.1
## [3] annotate_1.84.0 XML_3.99-0.18
## [5] AnnotationDbi_1.68.0 AUCell_1.28.0
## [7] here_1.0.1 data.table_1.17.0
## [9] ggpmisc_0.6.1 ggpp_0.5.8-1
## [11] gghighlight_0.4.1 readxl_1.4.5
## [13] openxlsx_4.2.8 car_3.1-3
## [15] carData_3.0-5 RColorBrewer_1.1-3
## [17] lubridate_1.9.4 forcats_1.0.0
## [19] purrr_1.0.4 readr_2.1.5
## [21] tidyr_1.3.1 tibble_3.2.1
## [23] tidyverse_2.0.0 DESeq2_1.46.0
## [25] SummarizedExperiment_1.36.0 Biobase_2.66.0
## [27] MatrixGenerics_1.18.1 matrixStats_1.5.0
## [29] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
## [31] IRanges_2.40.1 S4Vectors_0.44.0
## [33] BiocGenerics_0.52.0 EnhancedVolcano_1.24.0
## [35] ggrepel_0.9.6 ggplot2_3.5.1
## [37] patchwork_1.3.0 glmGamPoi_1.18.0
## [39] sctransform_0.4.1 SeuratObject_5.0.2
## [41] Seurat_4.4.0 stringr_1.5.1
## [43] dplyr_1.1.4 knitr_1.50
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.4.3
## [3] later_1.4.1 R.oo_1.27.0
## [5] cellranger_1.1.0 polyclip_1.10-7
## [7] lifecycle_1.0.4 rprojroot_2.0.4
## [9] globals_0.16.3 lattice_0.22-6
## [11] MASS_7.3-64 magrittr_2.0.3
## [13] plotly_4.10.4 sass_0.4.9
## [15] rmarkdown_2.29 jquerylib_0.1.4
## [17] yaml_2.3.10 httpuv_1.6.15
## [19] spam_2.11-1 zip_2.3.2
## [21] sp_2.2-0 spatstat.sparse_3.1-0
## [23] reticulate_1.41.0.1 DBI_1.2.3
## [25] cowplot_1.1.3 pbapply_1.7-2
## [27] abind_1.4-8 zlibbioc_1.52.0
## [29] Rtsne_0.17 mixtools_2.0.0.1
## [31] R.utils_2.13.0 GenomeInfoDbData_1.2.13
## [33] irlba_2.3.5.1 listenv_0.9.1
## [35] spatstat.utils_3.1-3 MatrixModels_0.5-3
## [37] goftest_1.2-3 spatstat.random_3.3-2
## [39] fitdistrplus_1.2-2 parallelly_1.42.0
## [41] DelayedMatrixStats_1.28.1 leiden_0.4.3.1
## [43] codetools_0.2-20 DelayedArray_0.32.0
## [45] tidyselect_1.2.1 UCSC.utils_1.2.0
## [47] farver_2.1.2 spatstat.explore_3.3-4
## [49] jsonlite_1.9.1 progressr_0.15.1
## [51] Formula_1.2-5 ggridges_0.5.6
## [53] survival_3.8-3 segmented_2.1-4
## [55] tools_4.4.3 ica_1.0-3
## [57] Rcpp_1.0.14 glue_1.8.0
## [59] gridExtra_2.3 SparseArray_1.6.2
## [61] xfun_0.51 withr_3.0.2
## [63] BiocManager_1.30.25 fastmap_1.2.0
## [65] SparseM_1.84-2 digest_0.6.37
## [67] timechange_0.3.0 R6_2.6.1
## [69] mime_0.12 colorspace_2.1-1
## [71] scattermore_1.2 tensor_1.5
## [73] RSQLite_2.3.9 spatstat.data_3.1-6
## [75] R.methodsS3_1.8.2 generics_0.1.3
## [77] httr_1.4.7 htmlwidgets_1.6.4
## [79] S4Arrays_1.6.0 uwot_0.2.3
## [81] pkgconfig_2.0.3 gtable_0.3.6
## [83] blob_1.2.4 lmtest_0.9-40
## [85] XVector_0.46.0 htmltools_0.5.8.1
## [87] dotCall64_1.2 scales_1.3.0
## [89] png_0.1-8 spatstat.univar_3.1-2
## [91] rstudioapi_0.17.1 tzdb_0.5.0
## [93] reshape2_1.4.4 nlme_3.1-167
## [95] cachem_1.1.0 zoo_1.8-14
## [97] KernSmooth_2.23-26 parallel_4.4.3
## [99] miniUI_0.1.1.1 pillar_1.10.1
## [101] grid_4.4.3 vctrs_0.6.5
## [103] RANN_2.6.2 promises_1.3.2
## [105] xtable_1.8-4 cluster_2.1.8
## [107] evaluate_1.0.3 cli_3.6.4
## [109] locfit_1.5-9.12 compiler_4.4.3
## [111] rlang_1.1.5 crayon_1.5.3
## [113] future.apply_1.11.3 labeling_0.4.3
## [115] plyr_1.8.9 stringi_1.8.4
## [117] viridisLite_0.4.2 deldir_2.0-4
## [119] BiocParallel_1.40.0 Biostrings_2.74.1
## [121] munsell_0.5.1 lazyeval_0.2.2
## [123] spatstat.geom_3.3-5 quantreg_6.1
## [125] Matrix_1.7-2 hms_1.1.3
## [127] sparseMatrixStats_1.18.0 bit64_4.6.0-1
## [129] future_1.34.0 KEGGREST_1.46.0
## [131] shiny_1.10.0 kernlab_0.9-33
## [133] ROCR_1.0-11 memoise_2.0.1
## [135] igraph_2.1.4 bslib_0.9.0
## [137] bit_4.6.0 polynom_1.4-1